The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
인스턴스 기능 기반 딥러닝 방법은 훈련 및 추적 중에 대상을 템플릿과 직접 비교하여 고속 객체 추적 시스템의 성능을 촉진합니다. 그러나 인간 시각 시스템의 관점에서 볼 때 대상에 대한 사전 지식도 추적 과정에서 중요한 역할을 합니다. 의미론적 지식과 인스턴스 특징을 통합하기 위해, 우리는 다양한 사전 지식과 해당 가정의 신뢰도를 기반으로 경계 상자를 동시에 출력하는 컨벌루션 네트워크 기반 객체 추적 프레임워크를 제안합니다. 실험 결과는 우리가 제안한 접근 방식이 대부분의 일상 개체를 다루는 작업을 추적하는 데 있어 다른 주요 방법보다 더 높은 정확성과 효율성을 유지한다는 것을 보여줍니다.
Suofei ZHANG
Nanjing University of Posts and Telecommunications
Bin KANG
Nanjing University of Posts and Telecommunications
Lin ZHOU
Southeast University
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Suofei ZHANG, Bin KANG, Lin ZHOU, "Object Tracking by Unified Semantic Knowledge and Instance Features" in IEICE TRANSACTIONS on Information,
vol. E102-D, no. 3, pp. 680-683, March 2019, doi: 10.1587/transinf.2018EDL8181.
Abstract: Instance features based deep learning methods prompt the performances of high speed object tracking systems by directly comparing target with its template during training and tracking. However, from the perspective of human vision system, prior knowledge of target also plays key role during the process of tracking. To integrate both semantic knowledge and instance features, we propose a convolutional network based object tracking framework to simultaneously output bounding boxes based on different prior knowledge as well as confidences of corresponding Assumptions. Experimental results show that our proposed approach retains both higher accuracy and efficiency than other leading methods on tracking tasks covering most daily objects.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2018EDL8181/_p
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@ARTICLE{e102-d_3_680,
author={Suofei ZHANG, Bin KANG, Lin ZHOU, },
journal={IEICE TRANSACTIONS on Information},
title={Object Tracking by Unified Semantic Knowledge and Instance Features},
year={2019},
volume={E102-D},
number={3},
pages={680-683},
abstract={Instance features based deep learning methods prompt the performances of high speed object tracking systems by directly comparing target with its template during training and tracking. However, from the perspective of human vision system, prior knowledge of target also plays key role during the process of tracking. To integrate both semantic knowledge and instance features, we propose a convolutional network based object tracking framework to simultaneously output bounding boxes based on different prior knowledge as well as confidences of corresponding Assumptions. Experimental results show that our proposed approach retains both higher accuracy and efficiency than other leading methods on tracking tasks covering most daily objects.},
keywords={},
doi={10.1587/transinf.2018EDL8181},
ISSN={1745-1361},
month={March},}
부
TY - JOUR
TI - Object Tracking by Unified Semantic Knowledge and Instance Features
T2 - IEICE TRANSACTIONS on Information
SP - 680
EP - 683
AU - Suofei ZHANG
AU - Bin KANG
AU - Lin ZHOU
PY - 2019
DO - 10.1587/transinf.2018EDL8181
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E102-D
IS - 3
JA - IEICE TRANSACTIONS on Information
Y1 - March 2019
AB - Instance features based deep learning methods prompt the performances of high speed object tracking systems by directly comparing target with its template during training and tracking. However, from the perspective of human vision system, prior knowledge of target also plays key role during the process of tracking. To integrate both semantic knowledge and instance features, we propose a convolutional network based object tracking framework to simultaneously output bounding boxes based on different prior knowledge as well as confidences of corresponding Assumptions. Experimental results show that our proposed approach retains both higher accuracy and efficiency than other leading methods on tracking tasks covering most daily objects.
ER -